Meta-Llama-3.1-8B-Instruct-bnb-4bit
| Property | Value |
|---|---|
| Parameter Count | 4.65B parameters |
| Context Length | 128k tokens |
| License | Llama 3.1 |
| Research Paper | View Paper |
What is Meta-Llama-3.1-8B-Instruct-bnb-4bit?
This is a 4-bit quantized version of Meta's Llama 3.1 8B instruction-tuned language model, optimized by Unsloth for improved memory efficiency and inference speed. It maintains the core capabilities of the original model while reducing memory usage by up to 70% and offering 2-5x faster performance.
Implementation Details
The model leverages bitsandbytes quantization to reduce the model size while preserving performance. It supports multiple tensor types (F32, BF16, U8) and is specifically designed for efficient deployment in resource-constrained environments.
- Optimized for 4-bit precision inference
- Supports 128k token context window
- Implements Grouped-Query Attention (GQA)
- Compatible with transformers library
Core Capabilities
- Multilingual support for 8 languages including English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai
- Strong performance on general knowledge tasks (69.4% on MMLU)
- Excels at mathematical reasoning with Chain-of-Thought (84.5% on GSM-8K)
- Code generation capabilities (72.6% pass@1 on HumanEval)
Frequently Asked Questions
Q: What makes this model unique?
The model combines the powerful capabilities of Llama 3.1 with memory-efficient 4-bit quantization, making it accessible for deployment on consumer hardware while maintaining strong performance across various tasks.
Q: What are the recommended use cases?
The model is well-suited for chatbots, code assistance, mathematical problem-solving, and multilingual applications. It's particularly valuable for scenarios requiring efficient deployment with limited computational resources.
